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[Calls] Preserve complete symbolic exceptional observations for class replay diagnostics

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
8/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
java, kotlin
Área
testing-qa

Línea de trabajo

This is internal symbolic-execution work in the usvm engine with a frozen experiment ledger and multiple gating contracts, so it is not a starting point for a first contribution. The payload names the sealed ledger selection-ledger-v7.json and V7_INFRASTRUCTURE_FRONTIER.md, which must not be overwritten. Done would mean exceptional observations match the exact native outcome, which the issue leaves to the maintainers to define.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Part of https://github.com/UnitTestBot/usvm/issues/483.

Observed boundary

Priority: P2. strict symbolic observation gap; native primary coverage already fixed in MR #479.

The original EMPTY_FRESH HashMap reproduction had five symbolic THREW candidates with incomplete strict snapshots; their native coverage was real.

Implementation scope

Provide comparable bounded exceptional observations for symbolic class scenarios: actual thrown kind/value and observable receiver state/effects at the throw point, including constructor failure. Reuse native error/value snapshots and existing engine catch semantics. Suspended prefixes must remain input-only and never acquire an invented terminal observation.

Acceptance criteria

  • Source cases throwing a primitive or modeled Error retain the observable state/effects and compare against the exact native outcome.
  • Constructor failures distinguish an unavailable external receiver from an incomplete observation; no invented successful construction.
  • Strict applicability/rejection is separate from complete agreeing native coverage. This work must not restore the old gate that discarded genuine native coverage on symbolic mismatch.

Existing work to reuse or coordinate

Evidence and delivery rules

Use the frozen v7 200-class ledger and the source-linked reproducer; do not overwrite sealed experiment output. Reuse existing loaders, domain projection, source instrumentation, candidate/replay contracts and bounded observations. Keep original class members and independently frozen domain policies. Run focused source/native checks for the changed contract and leave broad suites to CI; unsupported and newly exposed later gates remain recorded.

Local evidence: iccq2026-paper/experiments/class-coverage-round-robin-20261002/real-class-campaign-20261014/{original/selection-ledger-v7.json,V7_INFRASTRUCTURE_FRONTIER.md} and real-class-machine-diagnostic-20261014/revised-protocol-v8/README.md.

Lenguaje dominante
Kotlin
Estrellas
33
Forks
27
Merge medio
3 d 21 h
PR fusionados (30 d)
88

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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